Friday, May 26, 2017

Requirements for the evolution of evolvability

We have aready discussed how selection can bias variation to be more advantageous, but what are the the requirements for this to happen?
This paper from our colleagues at Southampton models gene regulatory network evolution to address which factors allow the generation of phenotypic variation "tailored" to environmental variation.

10:00 in Darwin's, with fika.

Paper available here

Abstract

One of the most intriguing questions in evolution is how organisms exhibit suitable pheno- typic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour develop- mental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regulari- ties, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting ‘quick fixes’ (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the sub- sequent failure to generalise.Wesupport the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that exist- ing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well- developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.

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